arXiv subjects

Mohammad Deghat

Publications and source records attributed to Mohammad Deghat.

17 recordsLinked to original sources

arXiv2026

MaCoPlanner: LLM-Assisted Manual-Compiled Task Planning with Proactive Safety Verification for Robotic Industrial Panel Operation

Robotic industrial panel operation requires not only accurate control localization but also compliance with operating procedures, safety rules, and device-state constraints distributed across heterogeneous manuals. This study presents MaCoPlanner, a task-planning framework built on knowledge compiled from equipment manuals that converts equipment manuals into a typed intermediate representation, retrieves task- and state-relevant evidence, and uses it to support plan generation. Before actuation, candidate plans are symbolically rolled out and checked against procedural and state-transition constraints; detected violations are localized and returned for targeted repair, while unresolved plans are rejected. A separate execution interface grounds verified symbolic actions to physical controls and updates the device state. Under an independent evaluation oracle, MaCoPlanner achieves a final violation rate of 2.7%, and 26.3% of the runs in the repair analysis are rejected after exhausting the refinement budget. Compared with Raw-Manual, task success increases from 62.8% to 84.4% on Level-2 tasks and from 25.9% to 43.2% on Level-3 tasks. Experiments on a controller-panel simulator without an attached industrial load further demonstrate integrated execution feasibility under representative interaction conditions, without claiming industrial deployment readiness.

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arXiv2026

Balancing a Flying Inverted Pendulum with an Unknown Length Using Model Predictive Control and a Genetic Algorithm Estimator

This paper proposes an online Genetic Algorithm (GA) estimator and a Model Predictive Control (MPC) approach to solve the flying inverted pendulum problem in a practical experiment where the pendulum length is unknown. The performance of the MPC approach was demonstrated on a practical system through disturbance and trajectory tracking tests to assess controller robustness and tracking accuracy. The convergence speed and accuracy of the online GA estimator were validated on a practical system using different initial conditions.

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arXiv2026

Bearing-Only Solution to the Fermat-Weber Location Problem for Unicycle Agent

This paper addresses bearing-only algorithms for solving the Fermat-Weber Location Problem (FWLP) with a unicycle agent. Unlike existing FWLP solutions for single- or double-integrator agents, our approach accounts for the nonholonomic constraints of wheeled robots. We first develop a bearing-only control law for the case with stationary beacons. Next, we consider saturated control inputs and propose a corresponding bearing-only control law. Finally, we address moving beacons with constant velocities and develop a control law that enables the unicycle agent to track the moving Fermat-Weber point. Both simulations and experiments are provided to demonstrate the effectiveness of the proposed methods.

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arXiv2026

Self-Predictive Representation for Autonomous UAV Object-Goal Navigation

Autonomous Unmanned Aerial Vehicles (UAVs) have revolutionized industries through their versatility with applications including aerial surveillance, search and rescue, agriculture, and delivery. Their autonomous capabilities offer unique advantages, such as operating in large open space environments. Reinforcement Learning (RL) empowers UAVs to learn intricate navigation policies, enabling them to optimize flight behavior autonomously. However, one of its main challenge is the inefficiency in using data sample to achieve a good policy. In object-goal navigation (OGN) settings, target recognition arises as an extra challenge. Most UAV-related approaches use relative or absolute coordinates to move from an initial position to a predefined location, rather than to find the target directly. This study addresses the data sample efficiency issue in solving a 3D OGN problem, in addition to, the formalization of the unknown target location setting as a Markov decision process. Experiments are conducted to analyze the interplay of different state representation learning (SRL) methods for perception with a model-free RL algorithm for planning in an autonomous navigation system. The main contribution of this study is the development of the perception module, featuring a novel self-predictive model named AmelPred. Empirical results demonstrate that its stochastic version, AmelPredSto, is the best-performing SRL model when combined with actor-critic RL algorithms. The obtained results show substantial improvement in RL algorithms' efficiency by using AmelPredSto in solving the OGN problem.

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arXiv2025

Integrated Localization and Path Planning for an Ocean Exploring Team of Autonomous Underwater Vehicles with Consensus Graph Model Predictive Control

Navigation of a team of autonomous underwater vehicles (AUVs) coordinated by an unmanned surface vehicle (USV) is efficient and reliable for deep ocean exploration. AUVs depart from and return to the USV after collaborative navigation, data collection, and ocean exploration missions. Efficient path planning and accurate localization are essential, the latter of which is critical due to the lack of global localization signals and poor radio frequency (RF) communication in deep waters. Inertial navigation and acoustic communication are common solutions for localization. However, the former is subject to odometry drifts, and the latter is limited to short distances. This paper proposes a systematic approach for localization-aware energy-efficient collision-free path planning for a USV-AUVs team. Path planning is formulated as finite receding horizon model predictive control (MPC) optimization. A dynamic-aware linear kinodynamic motion equation is developed. The mathematical formulation for the MPC optimization is effectively developed where localization is integrated as consensus graph optimization among AUV nodes. Edges in the optimized AUV-to-USV (A2U) and AUV-to-AUV (A2A) graphs are constrained to the sonar range of acoustic modems. The time complexity of the consensus MPC optimization problem is analyzed, revealing a nonconvex NP-hard problem, which is solved using sequential convex programming. Numerical simulation results are provided to evaluate the proposed method.

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arXiv2025

Bearing-Only Tracking and Circumnavigation of a Fast Time-Varied Velocity Target Utilising an LSTM

Bearing-only tracking, localisation, and circumnavigation is a problem in which a single or a group of agents attempts to track a target while circumnavigating it at a fixed distance using only bearing measurements. While previous studies have addressed scenarios involving stationary targets or those moving with an unknown constant velocity, the challenge of accurately tracking a target moving with a time-varying velocity remains open. This paper presents an approach utilising a Long Short-Term Memory (LSTM) based estimator for predicting the target's position and velocity. We also introduce a corresponding control strategy. When evaluated against previously proposed estimation and circumnavigation approaches, our approach demonstrates significantly lower control and estimation errors across various time-varying velocity scenarios. Additionally, we illustrate the effectiveness of the proposed method in tracking targets with a double integrator nonholonomic system dynamics that mimic real-world systems.

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arXiv2025

Predefined-Time Target Localization and Circumnavigation using Bearing-Only Measurements: Theory and Experiments

This paper investigates the problem of controlling an autonomous agent to simultaneously localize and circumnavigate an unknown stationary target using bearing-only measurements (without explicit differentiation). To improve the convergence rate of target estimation, we introduce a novel adaptive target estimator that enables the agent to accurately localize the position of the unknown target with a tunable, predefined convergence time. Following this, we design a controller integrated with the estimator to steer the agent onto a circular trajectory centered at the target with a desired radius. The predefined-time stability of the overall system including the estimation and control errors are rigorously analyzed. Extensive simulations and experiments using unmanned aerial vehicles (UAVs) illustrate the performance and efficacy of the proposed estimation and control algorithms.

math.OC↗

arXiv2025

Distributed Pose Graph Optimization using the Splitting Method based on the Alternating Direction Method of Multipliers

Distributed optimization aims to leverage the local computation and communication capabilities of each agent to achieve a desired global objective. This paper addresses the distributed pose graph optimization (PGO) problem under non-convex constraints, with the goal of approximating the rotation and translation of each pose given relevant noisy measurements. To achieve this goal, the splitting method based on the concepts of the alternating direction method of multipliers (ADMM) and Bregman iteration are applied to solve the rotation subproblems. The proposed approach enables the iterative resolution of constrained problems, achieved through solving unconstrained problems and orthogonality-constrained quadratic problems that have analytical solutions. The performance of the proposed algorithm is compared against two practical methods in pose graph optimization: the Distributed Gauss-Seidel (DGS) algorithm and the centralized pose graph optimizer with an optimality certificate (SE-Sync). The efficiency of the proposed method is verified through its application to several simulated and real-world pose graph datasets. Unlike the DGS method, our approach attempts to solve distributed PGO problems without relaxing the non-convex constraints.

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arXiv2024

FTISS Adaptive Bearing-Only Formation Tracking Control with Unknown Disturbance Rejection

This paper proposes a finite-time input-to-state stable (FTISS) bearing-only formation control law that rejects unknown constant disturbances. Unlike existing finite-time bearing-based formation control laws, which typically rely on the availability of a global coordinate frame and some information about the disturbances, our approach requires only local bearing vector measurements and does not necessitate the alignment of agent coordinate frames. The proposed control law guarantees that formation control errors converge to a neighborhood of zero in finite time, and subsequently converge to zero asymptotically. We first address the scenario where leaders are stationary and then extend the results to leaders moving with a constant velocity. Simulation and experimental results are presented to validate the effectiveness of the proposed control law.

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arXiv2024

How Can LLMs and Knowledge Graphs Contribute to Robot Safety? A Few-Shot Learning Approach

Large Language Models (LLMs) are transforming the robotics domain by enabling robots to comprehend and execute natural language instructions. The cornerstone benefits of LLM include processing textual data from technical manuals, instructions, academic papers, and user queries based on the knowledge provided. However, deploying LLM-generated code in robotic systems without safety verification poses significant risks. This paper outlines a safety layer that verifies the code generated by ChatGPT before executing it to control a drone in a simulated environment. The safety layer consists of a fine-tuned GPT-4o model using Few-Shot learning, supported by knowledge graph prompting (KGP). Our approach improves the safety and compliance of robotic actions, ensuring that they adhere to the regulations of drone operations.

cs.RO↗

arXiv2024

Distributed Robust Continuous-Time Optimization Algorithms for Time-Varying Constrained Cost

This paper presents a distributed continuous-time optimization framework aimed at overcoming the challenges posed by time-varying cost functions and constraints in multi-agent systems, particularly those subject to disturbances. By incorporating tools such as log-barrier penalty functions to address inequality constraints, an integral sliding mode control for disturbance mitigation is proposed. The algorithm ensures asymptotic tracking of the optimal solution, achieving a tracking error of zero. The convergence of the introduced algorithms is demonstrated through Lyapunov analysis and nonsmooth techniques. Furthermore, the framework's effectiveness is validated through numerical simulations considering two scenarios for the communication networks.

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arXiv2024

AA-DLADMM: An Accelerated ADMM-based Framework for Training Deep Neural Networks

Stochastic gradient descent (SGD) and its many variants are the widespread optimization algorithms for training deep neural networks. However, SGD suffers from inevitable drawbacks, including vanishing gradients, lack of theoretical guarantees, and substantial sensitivity to input. The Alternating Direction Method of Multipliers (ADMM) has been proposed to address these shortcomings as an effective alternative to the gradient-based methods. It has been successfully employed for training deep neural networks. However, ADMM-based optimizers have a slow convergence rate. This paper proposes an Anderson Acceleration for Deep Learning ADMM (AA-DLADMM) algorithm to tackle this drawback. The main intention of the AA-DLADMM algorithm is to employ Anderson acceleration to ADMM by considering it as a fixed-point iteration and attaining a nearly quadratic convergence rate. We verify the effectiveness and efficiency of the proposed AA-DLADMM algorithm by conducting extensive experiments on four benchmark datasets contrary to other state-of-the-art optimizers.

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arXiv2023

Communication-Free Bearing-Only Distributed Target Localization and Circumnavigation by Multiple Agents

This paper considers the problem of localization and circumnavigation of an unknown stationary target by a group of autonomous agents using only local bearing measurements. We assume that no direct communication or exchange of information is permitted among the group of agents and propose an algorithm to approximate the angular separation between agents and a distributed control law that forces the agents to circumnavigate the target at a desired orbit with any prescribed angular separation. Global asymptotic stability of the system is analyzed rigorously using Lyapunov theory, cascade control strategy, and perturbation method. The performance of the proposed algorithm is verified through simulations.

math.OC↗

arXiv2018

Detection and Mitigation of Biasing Attacks on Distributed Estimation Networks

The paper considers a problem of detecting and mitigating biasing attacks on networks of state observers targeting cooperative state estimation algorithms. The problem is cast within the recently developed framework of distributed estimation utilizing the vector dissipativity approach. The paper shows that a network of distributed observers can be endowed with an additional attack detection layer capable of detecting biasing attacks and correcting their effect on estimates produced by the network. An example is provided to illustrate the performance of the proposed distributed attack detector.

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arXiv2018

Closeness of Solutions for Singularly Perturbed Systems via Averaging

This paper studies the behavior of singularly perturbed nonlinear differential equations with boundary-layer solutions that do not necessarily converge to an equilibrium. Using the average of the fast variable and assuming the boundary layer solutions converge to a bounded set, results on the closeness of solutions of the singularly perturbed system to the solutions of the reduced average and boundary layer systems over a finite time interval are presented. The closeness of solutions error is shown to be of order O(\sqrt(\epsilon)), where \epsilon is the perturbation parameter.

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arXiv2016

Detection of Biasing Attacks on Distributed Estimation Networks

The paper addresses the problem of detecting attacks on distributed estimator networks that aim to intentionally bias process estimates produced by the network. It provides a sufficient condition, in terms of the feasibility of certain linear matrix inequalities, which guarantees distributed input attack detection using an $H_\infty$ approach.

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arXiv2014

Simultaneous Velocity and Position Estimation via Distance-only Measurements with Application to Multi-Agent System Control

This paper proposes a strategy to estimate the velocity and position of neighbor agents using distance measurements only. Since with agents executing arbitrary motions, instantaneous distance-only measurements cannot provide enough information for our objectives, we postulate that agents engage in a combination of circular motion and linear motion. The proposed estimator can be used to develop control algorithms where only distance measurements are available to each agent. As an example, we show how this estimation method can be used to control the formation shape and velocity of the agents in a multi agent system. Simulation results are provided to illustrate the performance of the proposed algorithm.

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